LLM vs RAG vs AI Agent vs Agentic AI
Four terms people mix up at work: what each means, when it matters for your job, and how to check AI exposure on your real tasks — free.
A large language model is the foundation layer: it's what understands and generates text, trained to predict language patterns well enough to follow instructions, reason step by step, and hold a short conversational memory. On its own, an LLM knows only what was in its training data, frozen at some past date -- ask it about something that happened last week, or about your company's internal documents, and it has no way to know.
Retrieval-augmented generation, RAG, is the fix for that specific gap: before generating an answer, the system searches an external knowledge base -- your documents, a database, the live web -- and feeds the relevant passages into the model's context so it answers from that material instead of guessing from memory. It's the difference between asking someone to recall a fact and handing them the actual document to read first; RAG is why a chatbot can accurately answer questions about a policy document it was never trained on.
An AI agent adds a different capability on top of either: the ability to act, not just answer. Given tools -- an API to call, a database to query, a file to write -- an agent can look at a task, decide what tool to use, execute it, check the result, and decide what to do next, looping until the task is done rather than stopping after one response. Agentic AI is the level above a single agent: multiple agents, each with a role, coordinating on a larger goal, with an orchestration layer deciding how work gets split up and handling the handoffs between them. It's a genuine step up in complexity and failure modes, not just "an agent, but bigger" -- which is why most real systems today are single agents with tools, not full agentic orchestration, despite how often the term gets used.
General AI-systems terminology, reflecting common industry usage as of 2026 rather than any single named source.
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